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Oct 5 • 3 tweets • 4 min read
Today in @Nature: #AlphaTensor, an AI system for discovering novel, efficient, and exact algorithms for matrix multiplication - a building block of modern computations. AlphaTensor finds faster algorithms for many matrix sizes: dpmd.ai/dm-alpha-tensor & dpmd.ai/nature-alpha-t… 1/
Starting from scratch, #AlphaTensor gradually improves from finding the “class-room” algorithm to discovering historical algorithms such as Strassen’s - eventually surpassing them. This work is an example of how AI can advance the field of algorithmic discovery. 2/
By @AlhusseinFawzi, @matejbalog, Aja Huang, Thomas Hubert, @ber24, @BarekatainAmin, @SashaVNovikov, @franciscuto, @Mononofu, @GrzegorzMS, David Silver, @demishassabis, and @pushmeet. 3/3

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More from @DeepMind

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Jul 14
Working together with @YouTube’s product and engineering teams, we've helped optimise the decision-making processes that increase safety, decrease latency, and enhance the viewer, creator, and advertiser experience for all.

How? A 🧵...

dpmd.ai/dm-youtube 1/
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Apr 21
Recently the DeepMind Jax Ecosystem was joined by four new libraries: Mctx, KFAC-JAX, DM_AUX, and TF2JAX. See the thread 🧵 for more detail.

We hope that this fosters new research and applications in the ML community! 1/
Mctx provides AlphaZero and MuZero Monte Carlo tree search: dpmd.ai/mctx

Work by @fabiointheuk, Ivo Danihelka, @matteohessel, and @laurentsifre, with support from many others. 2/
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Work by Aleksandar Botev and James Martens. 3/
Read 5 tweets
DeepMind Profile picture
Mar 9
Congratulations to @demishassabis, John Jumper, and David Baker who will be awarded the Wiley Foundation 20th annual Wiley Prize in Biomedical Sciences on April 1: dpmd.ai/Wiley-Prize 🧵 1/7
Demis and John accept the award on behalf of the @DeepMind team who worked on #AlphaFold, which was recognised as a solution to the “protein folding problem” at CASP14 in Nov 2020: dpmd.ai/casp14_blog 2/7
From the start, we committed to giving broad access to our work and, in July 2021, we published our methods in @Nature along with the open source code.

Paper: dpmd.ai/alphafold-meth…
Open Source: dpmd.ai/alphafold-gith… 3/7 Image
Read 7 tweets
DeepMind Profile picture
Sep 29, 2021
From packing an umbrella to preparing for extreme conditions, predicting short term weather patterns is crucial for daily life.

New research with the @metoffice and SOTA model advances the science of Precipitation Nowcasting - the prediction of rain: dpmd.ai/nowcasting 1/4
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Compared to widely used-nowcasting methods, meteorologists from @metoffice rated this method as their 1st choice 89% of the time.

There's more to do but our researchers hope this will act as a base for future work & promote greater integration of ML & environmental science. 3/4
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Sep 9, 2021
Introducing the '21 DeepMind x @ai_ucl Reinforcement Learning Lecture Series, a comprehensive introduction to modern RL.

Follow along with our researchers are they explore Markov Decision Processes, sample-based learning algorithms & much more: dpmd.ai/2021RLseries 1/2 Image
Also find the full series via the DeepMind @YouTube channel: dpmd.ai/DeepMindxUCL21
In the first lecture of the series, Research Scientist Hado introduces the course and explores the fascinating connection between reinforcement learning and artificial intelligence: dpmd.ai/RLseries1

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DeepMind Profile picture
Aug 3, 2021
To tackle all the challenges we meet while solving intelligence, we need tools that are as adaptable as possible. Announcing the paper & code for Perceiver IO, an architecture that handles a wide range of data and tasks, all while scaling gracefully: dpmd.ai/perceiver-IO 1/4 Image
Perceiver IO has the benefits of the Perceiver - domain assumptions✖️, large data✓ - but can produce a huge variety of outputs. W/ one general architecture:

*SOTA on Sintel optical flow
*Beats BERT *without using tokens*
*Multimodal or simultaneous multi-task training

2/4
Perceiver IO can simplify how engineers build systems. For example, Transformers often group language inputs into smaller chunks (“tokenization”) before processing. But Perceiver IO does well without tokenization by learning how to process the raw inputs for itself. 3/4 Image
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